[digiKam-users] Face Recognition Workflow

Rhys Tyers rhystyers1 at gmail.com
Wed Aug 18 18:26:59 BST 2021


What is the correct way to train the face detection model when many of
the faces are poor quality images?

I have tens of thousands of photos which include hundreds of people many
hundreds of times each. Many of their faces in these photos are quite poor
quality (obscured, dark, far away).

When I start recognition it is often quite poor but once I have a few high
quality faces in the model it seems to work fairly well. However as I
accept more of the poor quality faces I find the recognition gets worse (I
assume because dark, far way, poor quality faces look very similar). People
for whom I've tagged a lot of poor quality images just start getting any
face suggested for them.

What is the intended workflow? The application prompts you to accept
recognised faces as face tags (and then they will get used in the
training), so that's what I've been doing.

But then there is this in the manual:* In case of unsatisfying results it
might be helpful to use Clear and rebuild all training data. One reason can
be that there are too many face tags assigned to a person which shows this
person in a way that doesn't really help the search algorithm, e.g. with
sunglasses, blurred, unusual colors, carnival make up, dark shaded areas in
the face, baby/kid/adult photographs mixed.*

So that implies that I should only add high quality faces to the face tags
and perhaps tag poor quality ones differently? Is there some way to mark a
face as "poor quality" so it is not used for training?

Thanks for any help.

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